Evaluation of Landslide Susceptibility by Multiple Adaptive Regression Spline Method

被引：0
|
作者：
Zhao Z. [1 ,3 ,4 ]
Zhang F. [2 ]
Zheng J. [1 ,3 ,4 ]
机构：
[1] Key Laboratory of Oasis Ecology Education, Xinjiang University, Urumqi
[2] Xinjiang Vocational and Technical College of Communications, Urumqi
[3] College of Resources and Environmental Sciences, Xinjiang University, Urumqi
[4] Key Laboratory of Intelligent City and Environmental Modeling, Xinjiang University, Urumqi
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2021年 / 46卷 / 03期
关键词：
Landslide susceptibility index; Logistic regression; Multivariate adaptive regression splines; Susceptibility zoning;
D O I：
10.13203/j.whugis20190136
中图分类号：
学科分类号：
摘要：
Objectives: According to general landslide susceptibility evaluation methods, landslide condition factors cannot be effectively selected. Methods: The prediction model of landslide susceptibility index (LSI) is constructed by multiple adaptive regression spline (MARS), and the landslide susceptibility condition factors are automatically selected, and the landslide susceptibility map is produced by 15 landslide susceptibility factors. In addition, the accuracy of the model is compared between logistic regression (LR) and MARS. Results: The results show that the accuracy of landslide susceptibility model constructed by MARS is better than LR. The accuracy of MARS success curve is 0.945 4, and the accuracy of MARS prediction rate curve is 0.923 8. At the same time, the model also selects the important influencing factors of landslide (elevation, slope angle, rainfall, distance to faults, NDVI, plan curvature, geological petrofabric). Conclusions: Research suggests that the MARS is an effective method for landslide prediction in study area and can provide decision support for reducing nature disaster. © 2021, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
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页码：442 / 450
页数：8
相关论文
共 37 条
  • [1] Cruden D M., A Simple Definition of a Landslide, Bulletin of Engineering Geology and the Environment, 43, 1, pp. 27-29, (1991)
  • [2] Shirzadi A, Bui D T, Pham B T, Et al., Shallow Landslide Susceptibility Assessment Using a Novel Hybrid Intelligence Approach[J], Environmental Earth Sciences, 76, 2, (2017)
  • [3] Pham B T, Prakash I, Bui D T., Spatial Prediction of Landslides Using a Hybrid Machine Learning Approach Based on Random Subspace and Classification and Regression Trees, Geomorphology, 303, pp. 256-270, (2018)
  • [4] Wang Y, Wu X, Chen Z, Et al., Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China[J], International Journal of Environmental Research and Public Health, 16, 3, (2019)
  • [5] Tao Shu, Hu Deyong, Zhao Wenji, Et al., Sensitivity Evaluation of Secondary Landslide Disaster Based on Information and Logistic Regression Model: A Case Study of Northern Wenchuan County, Geographical Research, 29, 9, pp. 1594-1605, (2010)
  • [6] Bui D T, Tuan T A, Klempe H, Et al., Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree, Landslides, 13, 2, pp. 361-378, (2016)
  • [7] Awawdeh M M, ElMughrabi M A, Atallah M Y., Landslide Susceptibility Mapping Using GIS and Weighted Overlay Method: A Case Study from North Jordan, Environmental Earth Sciences, 77, 21, (2018)
  • [8] Bourenane H, Bouhadad Y, Guettouche M S, Et al., GIS-Based Landslide Susceptibility Zonation Using Bivariate Statistical and Expert Approaches in the City of Constantine (Northeast Algeria), Bulletin of Engineering Geology and the Environment, 74, 2, pp. 337-355, (2015)
  • [9] Pourghasemi H R, Moradi H R, Aghda S M F., Landslide Susceptibility Mapping by Binary Logistic Regression, Analytical Hierarchy Process, and Statistical Index Models and Assessment of Their Performances, Natural Hazards, 69, 1, pp. 749-779, (2013)
  • [10] Shahabi H, Khezri S, Ahmad B B, Et al., Landslide Susceptibility Mapping at Central Zab Basin, Iran: A Comparison Between Analytical Hierarchy Process, Frequency Ratio and Logistic Regression Models, Catena, 115, pp. 55-70, (2014)
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